The 3-D reconstruction of volume data from 2-D x-ray projections has become important in many fields. In the medical imaging field, the reconstruction of vessel structures is especially helpful for treating aneurysms or stenoses in interventional angiography, for example.
A brief explanation of 3-D reconstruction of volume data from 2-D x-ray projections can be better understood by reference to prior art FIG. 1. In the x-ray process, a real-world object (the cube 10) is x-rayed. 2-D x-ray projection images are generated. On a computer, these projections are made up of discrete “pixels.” For the reconstruction process, from those 2-D x-ray projections a 3-D data volume (the cube 10, which is now a computer object) is calculated. On a computer, this data cube is discrete, made up of “voxels”. In the volume rendering process, the data volume is viewed on a 2-D computer monitor, i.e. one generates 2-D views 12 of the 3-D data volume 10.
The volume rendering process takes into account the following attributes which the voxels have:
1. Most important: the gray value (−CT value−Hounsfield value). Something that should be related to the original x-ray absorption of the object at this very spot.
2. Different gray values contribute according to the transfer function. Voxels having gray values where the transfer function is zero do not contribute to the rendering.
3. Opacity: Example—If there is a voxel with 100% opacity one will not see what is behind. The other way around: if the opacity is low, one can generate a glass-like impression.
4. Light effects, which are modeled with virtual light sources and are based on gray value gradients, etc.
Prior art FIG. 2 shows a viewing ray for ray casting through volume/object 10. The prior art algorithm for ray casting using a single volume is as follows:
for each image pixel ρ                initial color of pixel to black: cρ:=0        for each voxel vρi belonging to pixel ρ                    calculate the color value c(vρi) originating from voxel vρi             determining opacity o(vρi) within a range 0 . . . 1cρ:=cρ(1−o(vρi))+c(vρi)o(vρi))                        end        
end
For reconstructing vessel structures many x-ray images are taken from different positions around the object (e.g. a patient's head) and in two phases. During the first phase no contrast agent is applied whereas in the second phase contrast agent is used for making the vessels visible. The images from the first run (without contrast agent) are called mask images; and the images of the second run (with contrast agent being injected) are called fill images. The corresponding mask reconstruction for the mask images are shown in prior art FIG. 3 and the corresponding fill reconstruction for the fill images is shown in prior art FIG. 4.
Four possible ways of reconstructing images using the Siemens, AG prior art product (InSpace 3D, DynaCT), are:                From these images a volume data set can be reconstructed using only mask images. The resulting 3-D volume will show both bone 13 and metal implant structures 9 (e.g. coils in aneurysms, stents, wires). That reconstruction is called “native mask” (See 2-D FIG. 3).        From these images a volume data set can be reconstructed using only fill images. The resulting 3-D volume will show both bone 13, metal structures 9, and vessels 14. That reconstruction is called “native fill” (See 2-D FIG. 4).        A third possibility is to subtract the information of fill and mask images. In this case only the vessel structures 14 are visible. That reconstruction is called “subtracted” (See 2-D FIG. 5).        If the slice thickness is increased and a softer reconstruction kernel (a mathematical formula to transform data such as for different spatial resolution or contrast) is used, reconstructions with a better low contrast resolution can be achieved at the expense of spatial resolution. This type of reconstruction is usually only applied from a mask run in a native way, and is called “enhanced” (See 2-D FIG. 6 showing enhanced bone 15). The enhanced reconstruction is available with the Siemens AG product Dyna CT. This reconstruction sacrifices resolution but gains gray value resolution. This enhances seeing a low contrast object like a hemorrhage.        
When calculating the subtracted reconstruction described above as the third possibility, the method is done in three phases in the prior art. First, the reconstruction from the mask images is done. Secondly, the fill images is done. The reconstruction from the mask images is shown by the native mask reconstruction mentioned above in FIG. 3. The reconstruction from the fill images is shown by the native fill reconstruction explained above in FIG. 4. Thirdly, the subtracted reconstruction is finally computed by subtracting the native mask from the native fill reconstruction as shown in FIG. 5.
The reconstruction from the mask images as well as the reconstruction from the fill images in the prior art are stored as separate 3-D data sets for later use during visualization. The final subtracted result also is stored as a 3-D data set.
In the prior art the enhanced reconstruction is computed either with a modified reconstruction kernel starting from the same projection images as for the other reconstructions or by applying appropriate filter operations on the previously computed native reconstruction, as described above.
In the prior art, with the storage of all these data sets there exist several possibilities for visualization, which show vessel, bone, and low-contrast structures separately or in combination:                The subtracted reconstruction is used for hiding occluding bones (FIG. 5);        The fill reconstruction is used to show both, bones and vessels (FIG. 4);        The mask reconstruction is used to show bones and other objects like coils and stents (FIG. 3); and        The enhanced reconstruction is used to show low-contrast regions like tumors or bleedings (FIG. 6).        
For visualization, the 3-D data sets must be loaded into an application (InSpace 3D, Dyna CT) that is capable of producing 2-D projections or sections of the 3-D volume (e.g. Volume Rendering Technique (VRT), a way to visualize medical volume data, Maximum Intensity Projection (MIP)—good for showing bones, Multi-planar Reformatting (MPR) used for visualizing arbitrary plane slices through a cube in any direction, etc.). If the user is interested in vessel structures, he usually visualizes a subtracted reconstruction which has the advantage that no bone structures hide vessels. However, a big disadvantage is that the missing bone structures prevent from providing orientation help. If the user is interested in bone structures the user visualizes the native reconstruction. If the user is interested in tumors, bleedings or other low-contrast objects, he uses the enhanced reconstruction for visualization.
A problem in the above prior art is the simultaneous usage and visualization of the information contained in these data sets. Prior art techniques are known for attempting to solve this problem. For visualizing both bone and vessel structures the user previously had two possibilities which also can be combined:                The user edits the volume such that bone structures that would hide vessel structures are removed manually. This approach is very time consuming and requires a lot of experience.        The user tries to separate the bones from the vessels by applying different visualization parameters (e.g. color, transparency) to different ranges of x-ray density (or gray values). This approach is also very time consuming, and usually no clear separation is possible as the density values of bones and vessels overlap.        
In many cases the above approaches do not lead to a satisfactory result because either bony structures are spatially very close to vessel structures or the density of vessels does not differ much from the density of bones.
The visualization of enhanced reconstructions has not been used in the prior art in combination with native or subtracted reconstructions.
It is also known to invoke each type of reconstruction separately. It was not previously known to simultaneously generate different types of reconstructed data sets out of a single acquisition run.